import pandas as pd
import seaborn as sns
import plotly.express as px

import matplotlib.pyplot as plt

import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

stocks = px.data.stocks()
stocks.head()

	date	GOOG	AAPL	AMZN	FB	NFLX	MSFT
0	2018-01-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2018-01-08	1.018172	1.011943	1.061881	0.959968	1.053526	1.015988
2	2018-01-15	1.032008	1.019771	1.053240	0.970243	1.049860	1.020524
3	2018-01-22	1.066783	0.980057	1.140676	1.016858	1.307681	1.066561
4	2018-01-29	1.008773	0.917143	1.163374	1.018357	1.273537	1.040708

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

from matplotlib.pyplot import MultipleLocator

x = stocks['date']
y = stocks['GOOG']

fig, ax= plt.subplots()
ax.plot(x,y,linewidth=2.0)
plt.title('GOOG stock')
plt.xlabel('date',fontsize = 16)
plt.ylabel('values',fontsize = 16)
x_major_locator=MultipleLocator(14)
ax.xaxis.set_major_locator(x_major_locator)
plt.rcParams['figure.figsize']=(18,10)
plt.show()

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

from matplotlib import pyplot as plt

y1=stocks['AAPL']
y2=stocks['AMZN']
y3=stocks['FB']
y4=stocks['NFLX']
y5=stocks['MSFT']

plt.figure(figsize=(16,10))
plt.plot(x,y,label='GOOG',linewidth=2,linestyle='dashdot',marker='|',color='pink')
plt.plot(x,y1,label='AAPL',linewidth=2,linestyle='dotted',marker='.',color='red')
plt.plot(x,y2,label='AMZN',linewidth=2,linestyle='dashed',marker=',',color='blue')
plt.plot(x,y3,label='FB',linewidth=2,linestyle='solid',marker='o',color='green')
plt.plot(x,y4,label='NFLX',linewidth=2,linestyle='-',marker='v',color='yellow')
plt.plot(x,y5,label='MSFT',linewidth=2,linestyle='--',marker='1',color='black')
plt.title('stocks')
plt.xlabel('date', fontsize = 16)
plt.ylabel('values', fontsize = 16)
plt.legend(prop = {'size':12})
x_major_locator=MultipleLocator(14)
ax=plt.gca()
ax.xaxis.set_major_locator(x_major_locator)
plt.show()
#plt.savefig(r'C:\Users\sc35131\Desktop\wangyao\Cell_consistence_alogrithm\shang\1234567890',bbox_inches = 'tight',pad_inches = 0,dpi =350)

Seaborn

First, load the tips dataset

tips = sns.load_dataset('tips')
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

• Are there differences between male and female when it comes to giving tips?
• What attribute correlate the most with tip?

# Question: What is the relationship between total_bill and tip?
sns.scatterplot(x='total_bill', y='tip', data=tips)
plt.show()

Plotly Express

Question 4:

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

• Turn stocks dataframe into a structure that can be picked up easily with plotly express

df = px.data.stocks() 
fig = px.line(df, x="date", y=["GOOG","AAPL","AMZN","FB","NFLX","MSFT"])
fig.update_yaxes(title_text="values")
fig.show()

The tips dataset

df = px.data.tips() 
fig = px.scatter(df, x="total_bill", y="tip")
fig.show()

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

• Extract the 2007 year data from the dataframe. You have to process the data accordingly
• use plotly bar
• Add different colors for different continents
• Sort the order of the continent for the visualisation. Use axis layout setting
• Add text to each bar that represents the population

df = px.data.gapminder()
df.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

df_2007 = df.query('year==2007')
df_continent=df_2007.groupby('continent').sum()
df_continent
fig = px.bar(df_continent,x='pop', y=df_continent.index, orientation='h',text='pop',color=df_continent.index)
#fig = px.bar(df_2007, x="pop", y="continent", orientation='h',text='pop',color='continent')
fig.update_yaxes(categoryorder="sum ascending")
fig.show()